Evolutionary Sound Synthesis: Rendering Spectrograms from Cellular Automata Histograms

Serquera, Jaime; Miranda, Eduardo Reck

doi:10.1007/978-3-642-12242-2_39

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…The implementation is done by MatLab to produce an output spectrum according to input (target) sound spectrum file. Other researchers [15] have been used method which is cellular automata (CA). This technique can be classified as one of a class of evolutionary algorithms for modeling dynamic systems that modify several characteristics with time.…”

Section: Evolutionary Sound Synthesismentioning

confidence: 99%

Automatic sound synthesis using the fly algorithm

et al. 2020

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Our study is demonstrated a new type of evolutionary sound synthesis method. This work based on the fly algorithm, a cooperative co-evolution algorithm; it is derived from the Parisian evolution approach. The algorithm has relatively amended the position of individuals (the Flies) represented by 3-D points. The fly algorithm has successfully investigated in different applications, starting with a real-time stereo vision for robotics. Also, the algorithm shows promising results in tomography to reconstruct 3-D images. The final application of the fly algorithm was generating artistic images, such as digital mosaics. In all these applications, the flies' representation started for simple, 3-D points, to complex one, the structure of 9-elements. Our method follows evolutionary digital art with the fly algorithm in representing the pattern of the flies. They represented in a way of having their structure. This structure includes position, colour, rotation angle, and size. Our algorithm has the benefit of graphics processing units (GPUs) to generate the sound waveform using the modern OpenGL shading language.

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Section: Evolutionary Sound Synthesismentioning

confidence: 99%

Automatic sound synthesis using the fly algorithm

et al. 2020

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“…The growth model used in this research is the one we developed in [9] as an extended version of the multitype voter model.…”

Section: Ca Growth Modelmentioning

confidence: 99%

Algorithmic sound composition using coupled cellular automata

Serquera

Miranda

2011

Chaos Theory

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Abstract:In this paper we introduce a new approach to algorithmic sound composition using a bespoke technique combining coupled Cellular Automata (CA) and Histogram Mapping Synthesis. Two CA are used: a hodge podge machine and a growth model. The latter serves as control of the former. The hodge podge machine can exhibit different kinds of behaviour depending on the values of a set of rule parameters. Our method explores the fact that different simultaneous behaviours can be evolved within the same automaton if we bring into play different sets of parameter values. However, we restrict the number of parameter sets to two. Therefore, the CA growth model will have only two states and will delimit two dynamic zones in the hodge podge machine, each of which governed by a different set of parameter values. The predictable evolution of the two zones will produce a controlled dynamic sound spectrum. Among all the possibilities that this process affords for the composition of a variety of sounds algorithmically, we highlight its application to the attack portion of a sound, making it dynamically more complex than the rest of the sound.

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“…This search for input parameters is then a good candidate for an automatic optimization scheme. Thus, diverse optimization methods have been used for automatic calibration, such as Particle Swarm [11], HMM [34], Neural Nets [27], Cellular Automata [29] and Genetic Algorithms [12]. It has been suggested that evolutionary approaches such as Genetic Algorithms (GAs) are performing the best to matching musical instrument tones [27].…”

Section: Introductionmentioning

confidence: 99%

Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming

Macret

Pasquier

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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A sound synthesizer can be defined as a program that takes a few input parameters and returns a sound. The general sound synthesis problem could then be formulated as: given a sound (or a set of sounds) what program and set of input parameters can generate that sound (set of sounds)? We propose a novel approach to tackle this problem in which we represent sound synthesizers using Pure Data (Pd), a graphic programming language for digital signal processing. We search the space of possible sound synthesizers using Coevolutionary Mixed-typed Cartesian Genetic Programming (MT-CGP), and the set of input parameters using a standard Genetic Algorithm (GA). The proposed algorithm coevolves a population of MT-CGP graphs, representing the functional forms of synthesizers, and a population of GA chromosomes, representing their inputs parameters. A fitness function based on the Mel-frequency Cepstral Coefficients (MFCC) evaluates the distance between the target and produced sounds. Our approach is capable of suggesting novel functional forms and input parameters, suitable to approximate a given target sound (and we hope in future iterations a set of sounds). Since the resulting synthesizers are presented as Pd patches, the user can experiment, interact with, and reuse them.

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Evolutionary Sound Synthesis: Rendering Spectrograms from Cellular Automata Histograms

Cited by 9 publications

References 10 publications

Automatic sound synthesis using the fly algorithm

Automatic sound synthesis using the fly algorithm

Algorithmic sound composition using coupled cellular automata

Automatic design of sound synthesizers as pure data patches using coevolutionary mixed-typed cartesian genetic programming

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